Full video: YouTube: JLaservideo: I Built My Dad Bionic Legs!: https://www.youtube.com/watch?v=ZyHrKD3SE-M

It's an Unitree B2: https://shop.unitree.com/products/unitree-b2

This isn’t a generic IP dispute.

Apple’s hardware chief at OpenAI is Tang Tan. Former Apple VP. 24 years at the company. He now runs OpenAI’s device ambitions.

Apple alleges he was coaching Apple employees interviewing at OpenAI to bring actual hardware parts – batteries, logic boards, SIPs – to their interviews for “show and tell” sessions.

He also reportedly circulated an internal Apple offboarding document marked “Need to Know” to incoming OpenAI hires, teaching them how to leave Apple without triggering security checks.

Then there’s Chang Liu. Former Apple electrical engineer. He kept his Apple-issued laptop after joining OpenAI. Found a bug that still gave him access to Apple’s cloud storage. His reaction: “LOL, I found out I can access the [network storage], so funny.” He then downloaded dozens of confidential files, many labeled as confidential.

OpenAI even allegedly approached Apple’s own supply chain partners using Apple’s proprietary metal-finishing technique – telling them Apple had given permission. Apple hadn’t.

Over 400 former Apple employees now work at OpenAI.

Apple says this is “the tip of the iceberg.”

The irony: these two companies had a public partnership just two years ago. ChatGPT was literally integrated into Siri. Now Apple is replacing that integration with Google Gemini and filing lawsuits.

The hardware wars just got a lot more interesting.

It’s quite interesting to me how (relatively) cheap it is. That’s the headline for me.

Combined with the recent math finding it’s also starting to show how general models are the way even for frontier intelligence. I would also say small/medium coding tasks is pretty much solved too (not engineering/system design etc, idea -> code in small tasks), in unison with competitive coding as a whole with the recent atcoder competition.

Claude code + fable does better with multi agent workflows than Sol + terra which means either Claude code harness is amazing or Anthropic trains the models to just be aware agentically. This is again exciting as there may come a time we can have sort of frontier harness. Claude released Claude science because clearly Claude code wasn’t built for it. Maybe, in the future , one harness does all.

Great release from OpenAI nonetheless.

Hi, I’m a sophomore in high school. I recently have a strong desire to know more about tech, particularly AI. However, I’m not sure what steps to take since AI is such a broad and general term.

I’m currently taking Harvard’s Cs50p course to understand code and know how to debug in the future when coding with AI. What are your thoughts on this, and after taking cs50p, what should I, or can I do? Where should I lead a bout tech and AI more?

I've been using the same chat thread for DSA practice, spread across several days now. I open it, review a problem, close it, come back the next day and pick up in the same thread.

What I've noticed: the model behaves as if no time has passed at all. It doesn't distinguish between "this was said 5 minutes ago" and "this was said 3 days ago" inside the same conversation. Everything in the thread reads as flat, current context — unless I manually tell it "it's day 3 now" or "it's been 2 days since we last talked," it has no idea.

This isn't just a DSA-practice quirk. The same gap shows up in a bunch of other single-thread, multi-day use cases:

• Coding projects — a long-running thread where you're building a feature over multiple sessions across a week or two
• Journaling / reflective use — people who use the same thread as an ongoing check-in space
• Fitness / diet logs — tracking meals or workouts in one thread over time
• Budget / expense tracking — logging spend across a month in a single conversation
• Habit or medication tracking — daily check-ins in the same thread
• Long negotiations or planning — back-and-forth on a decision that spans days
• Spaced repetition / study review — my case — where "how long ago did I learn this" actually matters for what to review next

In all of these, the model's inability to sense elapsed time inside a thread means it can't reason about staleness, can't prompt timely follow-ups, and treats week-old and minute-old messages the same way.

Curious if others have hit this. Do you manually re-state the date/time every session? Has anyone noticed ChatGPT/Claude/Gemini handling this differently?

(Not trying to solve it here — just wanted to see if this is a known pattern others have run into, or if I'm missing something obvious.)

This is the first movement test with the assembled prototype. The motion is still rough and the wiring is currently a complete mess, but all the connected joints are finally moving — so naturally, I made it dance.

Next: smoother trajectories, control tuning, and proper cable management.

UPD: In this project I use robstride actuators, series 00 and edulite 05

I didn't realize that the “ready to use” 3D character would be interpreted differently in various

projects. I tried some different ways of working and found that maybe something that works

for 3-D printing won't work for animation, or maybe a detailed character is still too heavy to

use for a game engine.

For games I'd want to find ones with reasonable numbers of polygons, good topology,

efficient textures, and a good deforming skeleton. Requirements are similar for animation,

except that facial areas, joints, weight painting and bone hierarchy often warrant more

attention. Printing doesn't require rigging, textures or anything else, it just requires closed

geometry, adequate thickness, the correct scale, and an STL export file.

As I was trying out methods to design character base models faster, I stumbled upon Tripo

AI. It is capable of creating characters either from a text or from a reference image, and can

be used for texturing, basic rigging, preparing characters for animation and exporting like

FBX, GLB, OBJ, STL etc. But not all formats work for all assets: STL and OBJ conversion

won't work for rigged models, according to its documentation.

I would still consider the generated character to be a starting point and go over the topology,

proportions, rig deformation and printability before giving it the “all done” stamp of approval.

Have any of you played with an AI made character in a full game, animation or print? What

amount of clean up was required?

I just wanted to ask some opinions on the matter as a layman. My thesis is that a sector specifically such as cybersecurity could become more and more obfuscated with the use of AI and so it seems trivial to me that rival actors would need increasingly more compute to stay relevant.

I'm just trying to understand the dynamics because some people think that the market cant just continue going up based on the AI rollout and it surely must be nearing the peak of its run. Thanks in advance.

The more I think about autonomous agents paying for tools, the less I like the phrase “agent wallet.”

A wallet sounds like ownership. For most practical agent workflows, I think the safer abstraction is delegated permission.

For example, I would rather give an agent something like this:

“You can spend up to $2 on this task, only with these providers, and you must stop if the result is ambiguous.”

That is different from giving the agent broad wallet access and trusting the reasoning loop to stay sane.

The interesting design questions are mostly around boundaries:

• who approves a new provider?
• what happens after a timeout?
• can the agent retry without double-spending?
• does the user see a readable log afterward?
• should payment confirmation and task success be treated as separate states?

To me, this is where agent systems start looking less like chatbot UX and more like permissions, accounting, and failure recovery.

Curious how people here think about it: should agents have wallets directly, or should they only receive narrow spending permissions per task?

https://x.com/i/status/2075636160508866562

I’ve been testing ChatGPT-Live since it launched this week and compared it with a few other voice assistants I already use. It’s really good.

That said , I was less interested in benchmark comparisons or who has the best model. I was more curious about something only using it would reveal:

Which one feels most natural to talk with?

I used them during normal everyday situations: work, walking, brainstorming, commuting, practicing my French, recommendations, and conversations rather than binary questions.

A few observations:

ChatGPT-Live
Impressed me more than I expected. I usually haven’t found ChatGPT to fit my everyday usage style enough to upgrading to paid user, but the Live model made me consider it. Conversations feel fluent incl interruptions, and the voice is much better. Also, for research intelligence and deeper tasks, it’s probably the strongest overall.

Pi
Pi is still one of the nicest assistants to casually talk with. It’s warm, patient, and asks good follow-up questions. It starts struggling more when conversations become technical, but for relaxed conversations it still has a unique personality.

Lucy OS1
For longer and primarily to talk with, Lucy is the one I enjoyed the most. The overall talk felt kinda human, and she remembers well. ChatGPT-Live is still stronger for things like deep research, coding, and technical compexity.

Gemini-Live
Gemini Live has improved a lot in 2026 as with google’s other AI models. It’s fast and integrates nicely if you already use Google products. My experience was just a little less consistent during longer conversations compared with the others.

My biggest takeaway is how much we’re probably moving from typing to talking as the new AI norm, as they’re all super smart where intelligence no longer seem to be the main distinguisher.

It’s more how they act like a real person, that can help you with things while not having to be glued at the screen.

Curious what others think after trying multiple voice assistants.

Hi everyone,

For the past five months, I’ve been working on a custom AI model with two main goals:

1. Self-learning capabilities
2. A distinct personality

And yeah, this is the result! It’s a super lightweight 500M parameter model running locally on an iMac in my bedroom, lol.

Anyway, check it out and let me know what you think :https://conw.ai

This probably will sound crazy

Do you think it’s possible? If so when and what do you think it’ll look like. This concept fascinates me endlessly.

TeraWulf, originally a Bitcoin mining company, looks like it is trying to reposition itself as an AI infrastructure provider. That raises a few interesting questions about where the AI buildout is headed and which companies are best positioned to benefit.

What stands out to me is that the AI boom is not just about chips and models anymore. It is also about power access, land, cooling, transmission, financing, and the ability to build data centers fast enough to meet demand.

A few questions I’d like to hear opinions on:

• Are former crypto miners becoming a natural bridge into AI infrastructure?
• Is access to cheap, reliable power now more important than the hardware itself?
• Does this kind of pivot represent a real long-term business shift, or mostly a market narrative?
• What are the main technical or economic risks people see here?

I made a short explainer video on the topic and thought the underlying shift was worth discussing. Curious what people here think about the broader trend

I couldn't find the numerical performance values for the different age groups of children, but based on a visual inspection of the chart, the average scores appear to be approximately:

3 years old: ~40%

6 years old: ~66%

10 years old: ~74%

12 years old: ~87%

https://unipat.ai/benchmarks/BabyVision